213 citations · 303 across the 6 of their papers we have counts for
6 papers
Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework
Zhifeng Chen, Kamlesh Pawar, Kh Tohidul Islam +3
Motion artifacts in Magnetic Resonance Imaging (MRI) are one of the frequently occurring artifacts due to patient movements during scanning. Motion is estimated to be present in ap…
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification
Mevan Ekanayake, Kamlesh Pawar, Gary Egan +1
Deep learning (DL) models are capable of successfully exploiting latent representations in MR data and have become state-of-the-art for accelerated MRI reconstruction. However, und…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction
Mevan Ekanayake, Kamlesh Pawar, Mehrtash Harandi +2
Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI)…
MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach
Kamlesh Pawar, Zhaolin Chen, N. Jon Shah +1
Purpose: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts…
Multichannel Compressive Sensing MRI Using Noiselet Encoding
Kamlesh Pawar, Gary F. Egan, Jingxin Zhang
The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining th…